A Personalized Metasearch Engine Based on Multi-agent System (P)

Wang, Meijia; Liu, Qingshan; Lin, Yishuai

doi:10.18293/seke2018-082

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…Google Scholar is much lower than 1% for Cochrane reviews, with values ranging from a minimum of 0.05% to a maximum of 0.92% (Boeker et al, 2013;Gusenbauer and Haddaway, 2020). Wang et al (2019) suggested a personalization strategy for a metasearch engine based on a multi-agent system employing 50 sample queries split into three categoriestransactional, informational, and navigational to increase precision ratio. One of the studies used three form queries: Simple one-word queries, simple multi-word queries, and Complex multi-word queries to evaluate the precision in various search engines, including the metasearch engine (Pandey et al, 2015).…”

Section: Auxiliary Studies On Msesmentioning

confidence: 99%

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Measuring performance of metasearch engines to access information: an exploratory study based on precision metrics

Bhardwaj,

Kumar,

Nazim

2024

PMM

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PurposeThis paper evaluates the precision of four metasearch engines (MSEs) – DuckDuckGo, Dogpile, Metacrawler and Startpage, to determine which metasearch engine exhibits the highest level of precision and to identify the metasearch engine that is most likely to return the most relevant search results.Design/methodology/approachThe research is divided into two parts: the first phase involves four queries categorized into two segments (4-Q-2-S), while the second phase includes six queries divided into three segments (6-Q-3-S). These queries vary in complexity, falling into three types: simple, phrase and complex. The precision, average precision and the presence of duplicates across all the evaluated metasearch engines are determined.FindingsThe study clearly demonstrated that Startpage returned the most relevant results and achieved the highest precision (0.98) among the four MSEs. Conversely, DuckDuckGo exhibited consistent performance across both phases of the study.Research limitations/implicationsThe study only evaluated four metasearch engines, which may not be representative of all available metasearch engines. Additionally, a limited number of queries were used, which may not be sufficient to generalize the findings to all types of queries.Practical implicationsThe findings of this study can be valuable for accreditation agencies in managing duplicates, improving their search capabilities and obtaining more relevant and precise results. These findings can also assist users in selecting the best metasearch engine based on precision rather than interface.Originality/valueThe study is the first of its kind which evaluates the four metasearch engines. No similar study has been conducted in the past to measure the performance of metasearch engines.

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Section: Auxiliary Studies On Msesmentioning

confidence: 99%

“…Wang et al. (2019) suggested a personalization strategy for a metasearch engine based on a multi-agent system employing 50 sample queries split into three categories – transactional, informational, and navigational to increase precision ratio.…”

Section: Review Of Literaturementioning

confidence: 99%

Measuring performance of metasearch engines to access information: an exploratory study based on precision metrics

Bhardwaj,

Kumar,

Nazim

2024

PMM

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“…They are able to learn and act in their environment as presented in [22]. Agents may behave towards each other as collaborators, competitors or strangers [23,24]. A System multi agent is composed of the environment, Agents and the relationships that link them with each other in their environment, Behaviors and Operations of each agent [25,26].…”

Section: Multi Agent Systemmentioning

confidence: 99%

Hybrid Approach for Wind Turbines Power Curve Modeling Founded on Multi-Agent System and Two Machine Learning Algorithms, K-Means Method and the K-Nearest Neighbors, in the Retrieve Phase of the Dynamic Case Based Reasoning

2022

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Wind turbine power curve (WTPC) plays an important role for energy assessment, power forecasting and condition monitoring. The WTPC captures the nonlinear relationship between wind speed and output power. Many modeling approaches have been proposed by researches to improve the WTPC model performance. In this paper, we present a hybrid approach of wind turbines power curve modeling based on Case Based Reasoning approach, multi agent system, the K-Means unsupervised machine learning method, and then the supervised machine learning algorithm, which is the K-Nearest Neighbors KNN method. The both of the Machine Learning algorithms, K-means and KNN, are used in the retrieve step of the Dynamic Case Based Reasoning (DCBR) cycle to facilitate the search of wind turbines with similar characteristics to our target case. These wind turbines are first grouped into homogeneous classes and then sorted on the basis of a feature similarity measure using the K-Nearest Neighbors supervised machine learning method. Finally, a set of WTPC with similar characteristics of the target case are proposed.

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“…It has capacity to take a look at and act upon an environment [5]. A multi-agent system is a system composed of more than one interacting smart agents [6]. Using multi-agent structure to implement personalization mechanism for Web of things Search Engine (WoTSE) has great advantages.…”

Section: Introductionmentioning

confidence: 99%

WoT Search Engine based on Multi Agent System: A Conceptual Framework

Eddine

2022

Int. J. Interact. Mob. Technol.

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The rising incursions with embed and linked sensors cause the construction of the Internet of Things (IoT). It’s presenting a wide access to the physical world. We support that on head of this data, a Web of Things (WoT) is required. A framework that permits the achievement of programs related to sensors in the IoT. In this paper we present a conceptual model that describes Web of Things Search engine (WoTSE).Our WoTSE is based on artificial multi agent system with a modular architecture, addressing the key challenge of scalable search for rapidly changing content while leveraging existing Web infrastructure. Also, a model for searching in the WoT, namely WoTmAS2E, is proposed and experimentally evaluated using a prototype.

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A Personalized Metasearch Engine Based on Multi-agent System (P)

Cited by 3 publications

References 6 publications

Measuring performance of metasearch engines to access information: an exploratory study based on precision metrics

Measuring performance of metasearch engines to access information: an exploratory study based on precision metrics

Hybrid Approach for Wind Turbines Power Curve Modeling Founded on Multi-Agent System and Two Machine Learning Algorithms, K-Means Method and the K-Nearest Neighbors, in the Retrieve Phase of the Dynamic Case Based Reasoning

WoT Search Engine based on Multi Agent System: A Conceptual Framework

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